1356 – Early Attrition And Remission Outcomes Of Depression: An 18-month Nationwide Population-based Study
Bibliographic record
Abstract
Background Attrition can pose significant barriers to treatment success of depression; its contributing factors and impacts on treatment outcome need further clarification. Current study aimed to describe patterns of treatment attrition, to examine associated demographic and clinical determinants and to test whether and how attrition affects outcome of depression in a national cohort of adults treated for depression. Material and methods All adult patients prescribed with antidepressants for depression (n=216,557) in 2003 were identified from the National Health Insurance Research Database in Taiwan. Based on individuals’ clinical visit and antidepressant prescription, three attrition types, i.e., non-attrition, returning attrition and non-returning attrition, and their demographic/disease characteristics were identified. The relationships between attrition type and remission outcome over an 18-month follow-up period were further explored. Results Factors pertaining to disease characteristics (severity of depression, comorbidities, painful physical symptoms and past treatment history) and clinical practice (physician specialty and choice of antidepressants) were associated with attrition and remission outcome at 18-month follow-up. Patients remaining in treatment within the first three months were associated with higher odds of having sustained remission (OR=1.21; 99% CI: 1.16, 1.27) and lower odds of having relapses/recurrences (OR=0.20; 99% CI: 0.19, 0.21) over the 18-month period, compared to those returning attriters. Conclusions Early attrition has significant negative impacts on antidepressant treatment outcome; it hence needs to be minimized through shared decision-making, exchange of treatment preferences and proper patient-physician communication. Based on current understanding, further efforts to reduce early attrition are highly warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".